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All Journal Scientific Contributions Oil and Gas
Hari Sasongko
Infosys Consulting
Chemical Engineering, Chemistry & Bioengineering Energy

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Trap Prevention in Machine Learning in Prediction of Petrophysical Parameters: A Case Study in The Field X Adam Putra Pratama Zainuri; Pahala Dominicus Sinurat; Dedy Irawan; Hari Sasongko
Scientific Contributions Oil and Gas Vol 46 No 3 (2023)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.46.3.1586

Abstract

Petrophysical parameters such as porosity and water saturation are vital in the petroleum industry for reservoir characterization. These aspects are typically assessed through laboratorium measurements of core samples or intricate petrophysical calculations. Machine Learning (ML) offers a cost-effective and efficient approach as an alternative to conventional methods of predicting those parameters. However, developing ML models can be prone to the invisible traps such as overfitting, underfitting, feature selection, and feature importance. This study is intended to share how to identify the traps and its mitigation by establishing a synergistic workflow between ML and petrophysical theory. A model was developed based on data from several wells in X field, where they are randomized and split into test and train data. Well-log normalization preceded data splitting, and input features were normalized with outlier removal. A feature selection function was then employed to choose a specific amount of log data. Finally, the model selection function identified the highest-scoring model. Without a proper workflow, overfitting, irrelevant feature selection, and imprecise ranking issues emerged. However, with the proper workflow, these invisible traps were mitigated, even with a relatively small dataset. The final model could accurately predict porosity and water saturation
Porosity Estimation in A Natural CO2-Water Reservoir Using Integrated Density-Resistivity Log Approach Pahala Dominicus Sinurat; Hari Sasongko; Nabil Samawi
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1887

Abstract

Natural CO₂ reservoirs represent important analogues for Carbon Capture and Storage (CCS) and Carbon Capture, Utilization, and Storage (CCUS), as they provide direct evidence of long-term CO₂ retention and trapping mechanisms. This study assesses porosity in a natural carbonate CO₂ reservoir using an integrated density–resistivity log approach. Conventional porosity logs, such as density, neutron, and sonic, often overestimate porosity in carbonate systems due to their limited sensitivity to pore connectivity. To overcome this limitation, density log-derived total porosity was integrated with resistivity-derived effective porosity, allowing for the differentiation between connected and isolated pore systems. Fluid density estimations, including supercritical CO₂ and brine, were computed and validated against standard references to ensure accuracy. The results show that density-only porosity overestimates values by up to 10% in dolomitic intervals, whereas the integrated method provides estimates that are more consistent with core measurements. Isolated porosity, averaging 2% in the upper dolomite and 1.5% in the lower dolomite, was identified as a non-contributing pore volume for injectivity, although it remains relevant for storage capacity. These findings underscore the importance of integrated log interpretation for precise reservoir characterization and offer new insights into evaluating natural CO₂ reservoirs for long-term geological storage.
Co-Authors Adam Putra Pratama Zainuri Dedy Irawan Nabil Samawi Pahala Dominicus Sinurat Pahala Dominicus Sinurat